The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is processed by kPCA to reduce its dimensionality; then, simplified data is used for two separate DNNs for training (detection and diagnosis). The relative performance of the hybrid model is compared with conventional methods. Tennessee Eastman Process was used to confirm the efficacy of the model. The results show the reduction of input dimensionality increases classification accuracy. In addition, splitting detection and diagnosis into two DNNs results in reduced training times and increased classification accuracy. The proposed hybrid model serves as an important tool to detect the fault and take early corrective actions, thus enhancing process safety.

数字化过程系统日益复杂，需要先进的工具来及早检测和诊断故障，以保持安全运行。本研究提出了一种由内核主成分分析 (kPCA) 和 DNN 组成的混合模型，可用于检测和诊断各种过程中的故障。复杂数据经过kPCA处理，降维；然后，简化的数据用于两个单独的 DNN 进行训练（检测和诊断）。混合模型的相对性能与传统方法进行了比较。田纳西伊士曼过程被用来确认模型的有效性。结果表明，输入维数的减少提高了分类精度。此外，将检测和诊断拆分为两个 DNN 可减少训练时间并提高分类准确度。所提出的混合模型是检测故障和采取早期纠正措施的重要工具，从而提高过程安全性。